Datasets:

Genentech
/

alzheimers-variant-tutorial-data

license: mit
task_categories:
  - tabular-regression
tags:
  - biology
  - genomics
pretty_name: Alzheimer's GWAS variants (hg19)
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: variants.csv

alzheimer's-variant-tutorial-data

Dataset Summary

This dataset contains summary statistics for 1,000 genomic variants. Each row represents a single-nucleotide polymorphism (SNP) mapped to the hg19 reference genome.

Dataset Structure

Data Fields

Based on the header of variants.csv:

Column	Type	Description
`snpid`	string	Unique identifier in `chr:pos_ref_alt` format
`chrom`	string	Chromosome (e.g., `chr6`)
`pos`	int	Genomic position (hg19)
`alt`	string	Alternate allele (effect allele)
`ref`	string	Reference allele (non-effect allele)
`rsid`	string	Reference SNP cluster ID
`pval`	float	P-value of the association
`beta`	float	Regression coefficient (effect size)
`se`	float	Standard error of the beta

Usage

from datasets import load_dataset
dataset = load_dataset("Genentech/alzheimers-variant-tutorial-data", split="test")
df = dataset.to_pandas()
print(df.head())